Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
We see an interesting spread with an outlier to the right. Answer the following questions, please:
because we work with such large numbers that it would by messy if we wrote the numbers “out”, and not used the log10
I sort the dataset after higest to lowest, and because we wants to know the higest gdp, we filter by “gdpPercap” and the year 1952
gapminder%>%
filter(!is.na(gdpPercap))%>%
filter(year == 1952)%>%
arrange(desc(gdpPercap))
## # A tibble: 142 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Kuwait Asia 1952 55.6 160000 108382.
## 2 Switzerland Europe 1952 69.6 4815000 14734.
## 3 United States Americas 1952 68.4 157553000 13990.
## 4 Canada Americas 1952 68.8 14785584 11367.
## 5 New Zealand Oceania 1952 69.4 1994794 10557.
## 6 Norway Europe 1952 72.7 3327728 10095.
## 7 Australia Oceania 1952 69.1 8691212 10040.
## 8 United Kingdom Europe 1952 69.2 50430000 9980.
## 9 Bahrain Asia 1952 50.9 120447 9867.
## 10 Denmark Europe 1952 70.8 4334000 9692.
## # ℹ 132 more rows
Next, you can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Tasks:
options(scipen = 999)
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
geom_jitter(aes(color=continent),alpha=0.8)+
labs(title="PLOT, continents difficientet by color ",x="GDP/capital", y= "life expectency")+
scale_x_log10()
We use the same function as before, just using 2007 as the filter year
gapminder%>%
filter(!is.na(gdpPercap))%>%
filter(year == 2007)%>%
arrange(desc(gdpPercap))
## # A tibble: 142 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Norway Europe 2007 80.2 4627926 49357.
## 2 Kuwait Asia 2007 77.6 2505559 47307.
## 3 Singapore Asia 2007 80.0 4553009 47143.
## 4 United States Americas 2007 78.2 301139947 42952.
## 5 Ireland Europe 2007 78.9 4109086 40676.
## 6 Hong Kong, China Asia 2007 82.2 6980412 39725.
## 7 Switzerland Europe 2007 81.7 7554661 37506.
## 8 Netherlands Europe 2007 79.8 16570613 36798.
## 9 Canada Americas 2007 80.7 33390141 36319.
## 10 Iceland Europe 2007 81.8 301931 36181.
## # ℹ 132 more rows
The comparison would be easier if we had the two graphs together,
animated. We have a lovely tool in R to do this: the
gganimate package. Beware that there may be other packages
your operating system needs in order to glue interim images into an
animation or video. Read the messages when installing the package.
Also, there are two ways of animating the gapminder ggplot.
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
…
This plot collates all the points across time. The next step is to
split it into years and animate it. This may take some time, depending
on the processing power of your computer (and other things you are
asking it to do). Beware that the animation might appear in the bottom
right ‘Viewer’ pane, not in this rmd preview. You need to
knit the document to get the visual inside an html
file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
…
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Now, choose one of the animation options and get it to work. You may
need to troubleshoot your installation of gganimate and
other packages
in sync with the animation? (Hint:
search labeling for transition_states() and
transition_time() functions respectively)
library(gganimate)
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
transition_time(year) +
labs(title = 'Year: {frame_time}')
anim2
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
transition_states(country) +
labs(title = 'country: {closest_state}')
anim2
options(scipen=999)
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
transition_time(year) +
geom_jitter(aes(color=continent),alpha=0.8)+
labs(x="GDP/capital", y= "life expectency")+
labs(title = 'Year: {frame_time}')
anim2
gapminder_unfiltered
dataset and download more at https://www.gapminder.org/data/ ]how do you make a illustration of the development in Denmark?
løsning:
library(dplyr)
DK <- gapminder%>%
select(country,lifeExp,gdpPercap,year,pop)%>%
filter(country == "Denmark")
DK
## # A tibble: 12 × 5
## country lifeExp gdpPercap year pop
## <fct> <dbl> <dbl> <int> <int>
## 1 Denmark 70.8 9692. 1952 4334000
## 2 Denmark 71.8 11100. 1957 4487831
## 3 Denmark 72.4 13583. 1962 4646899
## 4 Denmark 73.0 15937. 1967 4838800
## 5 Denmark 73.5 18866. 1972 4991596
## 6 Denmark 74.7 20423. 1977 5088419
## 7 Denmark 74.6 21688. 1982 5117810
## 8 Denmark 74.8 25116. 1987 5127024
## 9 Denmark 75.3 26407. 1992 5171393
## 10 Denmark 76.1 29804. 1997 5283663
## 11 Denmark 77.2 32167. 2002 5374693
## 12 Denmark 78.3 35278. 2007 5468120
Jeg plotter data fra Danmark
options(scipen=999)
anim2 <- ggplot(DK, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
transition_time(year) +
labs(x="GDP/capital", y= "life expectency")+
labs(title = "Denmark 'Year: {frame_time}'")
anim2
And it is working!!